These days what is known as a filtered back projection (FBP) method is used as the standard method for reconstructing CT scanned image data from X-ray CT data records of a computerized tomography device (CT device). In this method, the projection measured data acquired from the computerized tomography scanner is conventionally firstly pre-processed in order to free it as far as possible from noise. What is known as a “rebinning” step is then carried out in which the data generated with the beam that spreads from the source in a fan-shaped manner is rearranged such that it is in a form as if the detector had been struck by an X-ray wave front tapering parallel to the detector.
The data is then transformed into the frequency range. Filtering takes place in the frequency range by using a convolutional kernel which in most devices the operator can nowadays freely select from a menu using the user interface. Up to 80 different convolutional kernels are currently offered on some systems. The user can influence the image characteristics via the choice of convolutional kernel. Image characteristics include not just the image definition but also, for example, the image noise, granularity, texture and behavior in low-frequency bands, etc.
The user can therefore select for example whether he wants to reconstruct a very soft image or a very sharp image which, however, has greater granularity. The operator's selection can depend inter alia on the measuring situation, for example on which region of the images should be particularly well depicted and according to which objects or lesions are being sought. The filtered data is then inversely transformed. Using the data that has been re-sorted and filtered in this way back projection then takes place to the individual voxels within the volume of interest.
Owing to its approximative mode of operation problems with what are referred to as low-frequency cone beam artifacts and spiral artifacts occur with the conventional FBP methods, however. Furthermore, image definition is always linked to image noise in the case of conventional FBP methods. The greater the sharpness achieved, the higher the image noise also is, and vice versa.
Iterative reconstruction methods have therefore recently been developed with which these limitations may be eliminated. With such iterative reconstruction methods initial image data is firstly reconstructed from the projection measured data. A convolutional back projection method for example can be used for this purpose.
Synthetic projection data is then generated from this initial image data using a “projector” (projection operator), which is designed to mathematically depict the measuring system as well as possible. The difference from the measured signals is then back-projected with the adjoint operator, and a residue image is thus reconstructed with which the initial image is updated. The updated image data can in turn be used to generate new synthetic projection data in a next iterative step using the projection operator, form therefrom the difference from the measured signals again and calculate a new residue image with which the image data of the current iterative stage is improved again, etc. Using this type of method it is possible to reconstruct image data that has relatively good definition but still has low image noise.
One drawback of this iterative method in contrast to the simple back projection mentioned in the introduction, however, lies in the fact that the operator no longer has any direct influence on the image characteristics. In iterative reconstruction the image characteristics are influenced by the projector and the associated back projector used, and by what is known as the regularization term with which the gray scale values of adjacent image voxels are weighted with a potential function within iteration in order to achieve sufficient stability in the reconstruction. It is unclear in this connection how the different components need to be parameterized in detail to achieve certain image characteristics.